{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "NC-jvvwAyW4D"
      },
      "source": [
        "##### Copyright 2019 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "form",
        "colab": {},
        "colab_type": "code",
        "id": "bw8YpxF4yT8a"
      },
      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "nd8ddC7NQ8uZ"
      },
      "source": [
        "# Transformer Chatbot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "YcK7-_3RxRvK"
      },
      "source": [
        "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/examples/blob/master/community/en/transformer_chatbot.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/examples/blob/master/community/en/transformer_chatbot.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "\u003c/table\u003e"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "rHMPkA2eQrpT"
      },
      "source": [
        "This tutorial trains a \u003ca href=\"https://arxiv.org/abs/1706.03762\" class=\"external\"\u003eTransformer model\u003c/a\u003e to be a chatbot. This is an advanced example that assumes knowledge of [text generation](https://tensorflow.org/alpha/tutorials/text/text_generation), [attention](https://www.tensorflow.org/alpha/tutorials/text/nmt_with_attention) and [transformer](https://www.tensorflow.org/alpha/tutorials/text/transformer).\n",
        "\n",
        "The core idea behind the Transformer model is *self-attention*—the ability to attend to different positions of the input sequence to compute a representation of that sequence. Transformer creates stacks of self-attention layers and is explained below in the sections *Scaled dot product attention* and *Multi-head attention*.\n",
        "\n",
        "Note: The model architecture is identical to the example in [Transformer model for language understanding](https://www.tensorflow.org/alpha/tutorials/text/transformer), and we demonstrate how to implement the same model in the Functional approach instead of Subclassing."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 935
        },
        "colab_type": "code",
        "id": "mb_5bl7G_n30",
        "outputId": "f704051a-2787-4cda-bee4-1e280215f7a5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting tf-nightly-gpu-2.0-preview==2.0.0.dev20190520\n",
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            "  Downloading https://files.pythonhosted.org/packages/67/b2/0f71ca90b0ade7fad27e3d20327c996c6252a2ffe88f50a95bba7434eda9/wrapt-1.11.1.tar.gz\n",
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            "Collecting tb-nightly\u003c1.15.0a0,\u003e=1.14.0a0 (from tf-nightly-gpu-2.0-preview==2.0.0.dev20190520)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f9/68/a14620bfb042691f532dcde8576ff82ee82e4c003cdc0a3dbee5f289cee6/google_pasta-0.1.6-py3-none-any.whl (51kB)\n",
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            "Collecting tensorflow-estimator-2.0-preview (from tf-nightly-gpu-2.0-preview==2.0.0.dev20190520)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/71/e7/779651eca277d48486ae03d007162d37c93449bc29358fbe748e13639734/tensorflow_estimator_2.0_preview-1.14.0.dev2019052000-py2.py3-none-any.whl (427kB)\n",
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            "Building wheels for collected packages: wrapt\n",
            "  Building wheel for wrapt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Stored in directory: /root/.cache/pip/wheels/89/67/41/63cbf0f6ac0a6156588b9587be4db5565f8c6d8ccef98202fc\n",
            "Successfully built wrapt\n",
            "\u001b[31mERROR: thinc 6.12.1 has requirement wrapt\u003c1.11.0,\u003e=1.10.0, but you'll have wrapt 1.11.1 which is incompatible.\u001b[0m\n",
            "Installing collected packages: wrapt, tb-nightly, google-pasta, tensorflow-estimator-2.0-preview, tf-nightly-gpu-2.0-preview\n",
            "  Found existing installation: wrapt 1.10.11\n",
            "    Uninstalling wrapt-1.10.11:\n",
            "      Successfully uninstalled wrapt-1.10.11\n",
            "Successfully installed google-pasta-0.1.6 tb-nightly-1.14.0a20190520 tensorflow-estimator-2.0-preview-1.14.0.dev2019052000 tf-nightly-gpu-2.0-preview-2.0.0.dev20190520 wrapt-1.11.1\n"
          ]
        }
      ],
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "\n",
        "!pip install tf-nightly-gpu-2.0-preview==2.0.0.dev20190520\n",
        "import tensorflow as tf\n",
        "tf.random.set_seed(1234)\n",
        "\n",
        "!pip install tfds-nightly\n",
        "import tensorflow_datasets as tfds\n",
        "\n",
        "import os\n",
        "import re\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "y0AqALdZCbCW"
      },
      "source": [
        "##Prepare Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "awb4RH3XCobf"
      },
      "source": [
        "We will use the conversations in movies and TV shows provided by [Cornell Movie-Dialogs Corpus](https://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html), which contains more than 220 thousands conversational exchanges between more than 10k pairs of movie characters, as our dataset.\n",
        "\n",
        "`movie_conversations.txt` contains list of the conversation IDs and `movie_lines.text` contains the text of assoicated with each conversation ID. For further  information regarding the dataset, please check the README file in the zip file.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "colab_type": "code",
        "id": "S17Nfn6W_vhd",
        "outputId": "34908ae4-22f5-40bc-b93e-2f1c96e76692"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from http://www.cs.cornell.edu/~cristian/data/cornell_movie_dialogs_corpus.zip\n",
            "9920512/9916637 [==============================] - 1s 0us/step\n"
          ]
        }
      ],
      "source": [
        "path_to_zip = tf.keras.utils.get_file(\n",
        "    'cornell_movie_dialogs.zip',\n",
        "    origin=\n",
        "    'http://www.cs.cornell.edu/~cristian/data/cornell_movie_dialogs_corpus.zip',\n",
        "    extract=True)\n",
        "\n",
        "path_to_dataset = os.path.join(\n",
        "    os.path.dirname(path_to_zip), \"cornell movie-dialogs corpus\")\n",
        "\n",
        "path_to_movie_lines = os.path.join(path_to_dataset, 'movie_lines.txt')\n",
        "path_to_movie_conversations = os.path.join(path_to_dataset,\n",
        "                                           'movie_conversations.txt')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "iZMuzj0cVr3E"
      },
      "source": [
        "### Load and preprocess data\n",
        "\n",
        "To keep this example simple and fast, we are limiting the maximum number of training samples to`MAX_SAMPLES=25000` and the maximum length of the sentence to be `MAX_LENGTH=40`.\n",
        "\n",
        "We preprocess our dataset in the following order:\n",
        "* Extract `MAX_SAMPLES` conversation pairs into list of `questions` and `answers.\n",
        "* Preprocess each sentence by removing special characters in each sentence.\n",
        "* Build tokenizer (map text to ID and ID to text) using [TensorFlow Datasets SubwordTextEncoder](https://www.tensorflow.org/datasets/api_docs/python/tfds/features/text/SubwordTextEncoder).\n",
        "* Tokenize each sentence and add `START_TOKEN` and `END_TOKEN` to indicate the start and end of each sentence.\n",
        "* Filter out sentence that has more than `MAX_LENGTH` tokens.\n",
        "* Pad tokenized sentences to `MAX_LENGTH`\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "_B147qKb_0ks"
      },
      "outputs": [],
      "source": [
        "# Maximum number of samples to preprocess\n",
        "MAX_SAMPLES = 50000\n",
        "\n",
        "def preprocess_sentence(sentence):\n",
        "  sentence = sentence.lower().strip()\n",
        "  # creating a space between a word and the punctuation following it\n",
        "  # eg: \"he is a boy.\" =\u003e \"he is a boy .\"\n",
        "  sentence = re.sub(r\"([?.!,])\", r\" \\1 \", sentence)\n",
        "  sentence = re.sub(r'[\" \"]+', \" \", sentence)\n",
        "  # replacing everything with space except (a-z, A-Z, \".\", \"?\", \"!\", \",\")\n",
        "  sentence = re.sub(r\"[^a-zA-Z?.!,]+\", \" \", sentence)\n",
        "  sentence = sentence.strip()\n",
        "  # adding a start and an end token to the sentence\n",
        "  return sentence\n",
        "\n",
        "\n",
        "def load_conversations():\n",
        "  # dictionary of line id to text\n",
        "  id2line = {}\n",
        "  with open(path_to_movie_lines, errors='ignore') as file:\n",
        "    lines = file.readlines()\n",
        "  for line in lines:\n",
        "    parts = line.replace('\\n', '').split(' +++$+++ ')\n",
        "    id2line[parts[0]] = parts[4]\n",
        "\n",
        "  inputs, outputs = [], []\n",
        "  with open(path_to_movie_conversations, 'r') as file:\n",
        "    lines = file.readlines()\n",
        "  for line in lines:\n",
        "    parts = line.replace('\\n', '').split(' +++$+++ ')\n",
        "    # get conversation in a list of line ID\n",
        "    conversation = [line[1:-1] for line in parts[3][1:-1].split(', ')]\n",
        "    for i in range(len(conversation) - 1):\n",
        "      inputs.append(preprocess_sentence(id2line[conversation[i]]))\n",
        "      outputs.append(preprocess_sentence(id2line[conversation[i + 1]]))\n",
        "      if len(inputs) \u003e= MAX_SAMPLES:\n",
        "        return inputs, outputs\n",
        "  return inputs, outputs\n",
        "\n",
        "\n",
        "questions, answers = load_conversations()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "colab_type": "code",
        "id": "mfOOK5f7Wm6c",
        "outputId": "dc2832bf-96b5-4718-9b0d-23d65c378d71"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Sample question: i really , really , really wanna go , but i can t . not unless my sister goes .\n",
            "Sample answer: i m workin on it . but she doesn t seem to be goin for him .\n"
          ]
        }
      ],
      "source": [
        "print('Sample question: {}'.format(questions[20]))\n",
        "print('Sample answer: {}'.format(answers[20]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "s6XX2udMTCQt"
      },
      "outputs": [],
      "source": [
        "# Build tokenizer using tfds for both questions and answers\n",
        "tokenizer = tfds.features.text.SubwordTextEncoder.build_from_corpus(\n",
        "    questions + answers, target_vocab_size=2**13)\n",
        "\n",
        "# Define start and end token to indicate the start and end of a sentence\n",
        "START_TOKEN, END_TOKEN = [tokenizer.vocab_size], [tokenizer.vocab_size + 1]\n",
        "\n",
        "# Vocabulary size plus start and end token\n",
        "VOCAB_SIZE = tokenizer.vocab_size + 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "colab_type": "code",
        "id": "h5h8pvRUTFt5",
        "outputId": "f56356ee-847f-4fae-ed2d-38d14208ffd7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Tokenized sample question: [4, 281, 3, 281, 3, 143, 395, 176, 3, 42, 4, 38, 8191, 2, 37, 873, 27, 2031, 3096, 1]\n"
          ]
        }
      ],
      "source": [
        "print('Tokenized sample question: {}'.format(tokenizer.encode(questions[20])))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "YESTPgeg_XgT"
      },
      "outputs": [],
      "source": [
        "# Maximum sentence length\n",
        "MAX_LENGTH = 40\n",
        "\n",
        "\n",
        "# Tokenize, filter and pad sentences\n",
        "def tokenize_and_filter(inputs, outputs):\n",
        "  tokenized_inputs, tokenized_outputs = [], []\n",
        "  \n",
        "  for (sentence1, sentence2) in zip(inputs, outputs):\n",
        "    # tokenize sentence\n",
        "    sentence1 = START_TOKEN + tokenizer.encode(sentence1) + END_TOKEN\n",
        "    sentence2 = START_TOKEN + tokenizer.encode(sentence2) + END_TOKEN\n",
        "    # check tokenized sentence max length\n",
        "    if len(sentence1) \u003c= MAX_LENGTH and len(sentence2) \u003c= MAX_LENGTH:\n",
        "      tokenized_inputs.append(sentence1)\n",
        "      tokenized_outputs.append(sentence2)\n",
        "  \n",
        "  # pad tokenized sentences\n",
        "  tokenized_inputs = tf.keras.preprocessing.sequence.pad_sequences(\n",
        "      tokenized_inputs, maxlen=MAX_LENGTH, padding='post')\n",
        "  tokenized_outputs = tf.keras.preprocessing.sequence.pad_sequences(\n",
        "      tokenized_outputs, maxlen=MAX_LENGTH, padding='post')\n",
        "  \n",
        "  return tokenized_inputs, tokenized_outputs\n",
        "\n",
        "\n",
        "questions, answers = tokenize_and_filter(questions, answers)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "colab_type": "code",
        "id": "pohHm8IRWlIH",
        "outputId": "de8b684d-a7f1-4188-c200-75d4f52d0479"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Vocab size: 8333\n",
            "Number of samples: 44095\n"
          ]
        }
      ],
      "source": [
        "print('Vocab size: {}'.format(VOCAB_SIZE))\n",
        "print('Number of samples: {}'.format(len(questions)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "S50jT4upWh5c"
      },
      "source": [
        "### Create `tf.data.Dataset`\n",
        "\n",
        "We are going to use the [tf.data.Dataset API](https://www.tensorflow.org/api_docs/python/tf/data) to contruct our input pipline in order to utilize features like caching and prefetching to speed up the training process.\n",
        "\n",
        "The transformer is an auto-regressive model: it makes predictions one part at a time, and uses its output so far to decide what to do next.\n",
        "\n",
        "During training this example uses teacher-forcing. Teacher forcing is passing the true output to the next time step regardless of what the model predicts at the current time step.\n",
        "\n",
        "As the transformer predicts each word, self-attention allows it to look at the previous words in the input sequence to better predict the next word.\n",
        "\n",
        "To prevent the model from peaking at the expected output the model uses a look-ahead mask.\n",
        "\n",
        "Target is divided into `decoder_inputs` which padded as an input to the decoder and `cropped_targets` for calculating our loss and accuracy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "colab_type": "code",
        "id": "pttC3XxgAXWQ",
        "outputId": "d18a6de6-fad3-416b-bea9-6326c0d53719"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0521 04:24:35.050999 140098170382208 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/data/util/random_seed.py:58: add_dispatch_support.\u003clocals\u003e.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ]
        }
      ],
      "source": [
        "BATCH_SIZE = 64\n",
        "BUFFER_SIZE = 20000\n",
        "\n",
        "# decoder inputs use the previous target as input\n",
        "# remove START_TOKEN from targets\n",
        "dataset = tf.data.Dataset.from_tensor_slices((\n",
        "    {\n",
        "        'inputs': questions,\n",
        "        'dec_inputs': answers[:, :-1]\n",
        "    },\n",
        "    {\n",
        "        'outputs': answers[:, 1:]\n",
        "    },\n",
        "))\n",
        "\n",
        "dataset = dataset.cache()\n",
        "dataset = dataset.shuffle(BUFFER_SIZE)\n",
        "dataset = dataset.batch(BATCH_SIZE)\n",
        "dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "colab_type": "code",
        "id": "mU8yNWpwPlS7",
        "outputId": "9643ad7a-8a34-4b0e-b435-a8086e8d16d2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u003cPrefetchDataset shapes: ({inputs: (None, 40), dec_inputs: (None, 39)}, {outputs: (None, 39)}), types: ({inputs: tf.int32, dec_inputs: tf.int32}, {outputs: tf.int32})\u003e\n"
          ]
        }
      ],
      "source": [
        "print(dataset)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "s9eeMPjGXmI1"
      },
      "source": [
        "## Attention\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "uctkwvPZVSzu"
      },
      "source": [
        "### Scaled dot product Attention\n",
        "\n",
        "The scaled dot-product attention function used by the transformer takes three inputs: Q (query), K (key), V (value). The equation used to calculate the attention weights is:\n",
        "\n",
        "$$\\Large{Attention(Q, K, V) = softmax_k(\\frac{QK^T}{\\sqrt{d_k}}) V} $$\n",
        "\n",
        "As the softmax normalization is done on the `key`, its values decide the amount of importance given to the `query`.\n",
        "\n",
        "The output represents the multiplication of the attention weights and the `value` vector. This ensures that the words we want to focus on are kept as is and the irrelevant words are flushed out.\n",
        "\n",
        "The dot-product attention is scaled by a factor of square root of the depth. This is done because for large values of depth, the dot product grows large in magnitude pushing the softmax function where it has small gradients resulting in a very hard softmax. \n",
        "\n",
        "For example, consider that `query` and `key` have a mean of 0 and variance of 1. Their matrix multiplication will have a mean of 0 and variance of `dk`. Hence, *square root of `dk`* is used for scaling (and not any other number) because the matmul of `query` and `key` should have a mean of 0 and variance of 1, so that we get a gentler softmax.\n",
        "\n",
        "The mask is multiplied with *-1e9 (close to negative infinity).* This is done because the mask is summed with the scaled matrix multiplication of `query` and `key` and is applied immediately before a softmax. The goal is to zero out these cells, and large negative inputs to softmax are near zero in the output."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "ENfqAFna_50H"
      },
      "outputs": [],
      "source": [
        "def scaled_dot_product_attention(query, key, value, mask):\n",
        "  \"\"\"Calculate the attention weights. \"\"\"\n",
        "  matmul_qk = tf.matmul(query, key, transpose_b=True)\n",
        "\n",
        "  # scale matmul_qk\n",
        "  depth = tf.cast(tf.shape(key)[-1], tf.float32)\n",
        "  logits = matmul_qk / tf.math.sqrt(depth)\n",
        "\n",
        "  # add the mask to zero out padding tokens\n",
        "  if mask is not None:\n",
        "    logits += (mask * -1e9)\n",
        "\n",
        "  # softmax is normalized on the last axis (seq_len_k)\n",
        "  attention_weights = tf.nn.softmax(logits, axis=-1)\n",
        "\n",
        "  output = tf.matmul(attention_weights, value)\n",
        "\n",
        "  return output"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "XwmOB9HvVbyh"
      },
      "source": [
        "### Multi-head attention\n",
        "\n",
        "\u003cimg src=\"https://www.tensorflow.org/images/tutorials/transformer/multi_head_attention.png\" width=\"500\" alt=\"multi-head attention\"\u003e\n",
        "\n",
        "\n",
        "Multi-head attention consists of four parts:\n",
        "* Linear layers and split into heads.\n",
        "* Scaled dot-product attention.\n",
        "* Concatenation of heads.\n",
        "* Final linear layer.\n",
        "\n",
        "Each multi-head attention block gets three inputs; Q (query), K (key), V (value). These are put through linear (Dense) layers and split up into multiple heads. \n",
        "\n",
        "The `scaled_dot_product_attention` defined above is applied to each head (broadcasted for efficiency). An appropriate mask must be used in the attention step.  The attention output for each head is then concatenated (using `tf.transpose`, and `tf.reshape`) and put through a final `Dense` layer.\n",
        "\n",
        "Instead of one single attention head, `query`, `key`, and `value` are split into multiple heads because it allows the model to jointly attend to information at different positions from different representational spaces. After the split each head has a reduced dimensionality, so the total computation cost is the same as a single head attention with full dimensionality."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "L9eYssGIAG4h"
      },
      "outputs": [],
      "source": [
        "class MultiHeadAttention(tf.keras.layers.Layer):\n",
        "\n",
        "  def __init__(self, d_model, num_heads, name=\"multi_head_attention\"):\n",
        "    super(MultiHeadAttention, self).__init__(name=name)\n",
        "    self.num_heads = num_heads\n",
        "    self.d_model = d_model\n",
        "\n",
        "    assert d_model % self.num_heads == 0\n",
        "\n",
        "    self.depth = d_model // self.num_heads\n",
        "\n",
        "    self.query_dense = tf.keras.layers.Dense(units=d_model)\n",
        "    self.key_dense = tf.keras.layers.Dense(units=d_model)\n",
        "    self.value_dense = tf.keras.layers.Dense(units=d_model)\n",
        "\n",
        "    self.dense = tf.keras.layers.Dense(units=d_model)\n",
        "\n",
        "  def split_heads(self, inputs, batch_size):\n",
        "    inputs = tf.reshape(\n",
        "        inputs, shape=(batch_size, -1, self.num_heads, self.depth))\n",
        "    return tf.transpose(inputs, perm=[0, 2, 1, 3])\n",
        "\n",
        "  def call(self, inputs):\n",
        "    query, key, value, mask = inputs['query'], inputs['key'], inputs[\n",
        "        'value'], inputs['mask']\n",
        "    batch_size = tf.shape(query)[0]\n",
        "\n",
        "    # linear layers\n",
        "    query = self.query_dense(query)\n",
        "    key = self.key_dense(key)\n",
        "    value = self.value_dense(value)\n",
        "\n",
        "    # split heads\n",
        "    query = self.split_heads(query, batch_size)\n",
        "    key = self.split_heads(key, batch_size)\n",
        "    value = self.split_heads(value, batch_size)\n",
        "\n",
        "    # scaled dot-product attention\n",
        "    scaled_attention = scaled_dot_product_attention(query, key, value, mask)\n",
        "\n",
        "    scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3])\n",
        "\n",
        "    # concatenation of heads\n",
        "    concat_attention = tf.reshape(scaled_attention,\n",
        "                                  (batch_size, -1, self.d_model))\n",
        "\n",
        "    # final linear layer\n",
        "    outputs = self.dense(concat_attention)\n",
        "\n",
        "    return outputs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "eDUX7Oa8Xudj"
      },
      "source": [
        "## Transformer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "x5QlgXsxYirg"
      },
      "source": [
        "### Masking\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "0CX5H8A-Wybj"
      },
      "source": [
        "`create_padding_mask` and `create_look_ahead` are helper functions to creating masks to mask out padded tokens, we are going to use these helper functions as `tf.keras.layers.Lambda` layers.\n",
        "\n",
        "Mask all the pad tokens (value `0`) in the batch to ensure the model does not treat padding as input."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "imCQ0jrvWhC7"
      },
      "outputs": [],
      "source": [
        "def create_padding_mask(x):\n",
        "  mask = tf.cast(tf.math.equal(x, 0), tf.float32)\n",
        "  # (batch_size, 1, 1, sequence length)\n",
        "  return mask[:, tf.newaxis, tf.newaxis, :]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "colab_type": "code",
        "id": "IrwtsqrfWd-3",
        "outputId": "b717fa76-3791-4f9d-e044-f0476528ce34"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[[[[0. 0. 1. 0. 1.]]]\n",
            "\n",
            "\n",
            " [[[1. 1. 1. 0. 0.]]]], shape=(2, 1, 1, 5), dtype=float32)\n"
          ]
        }
      ],
      "source": [
        "print(create_padding_mask(tf.constant([[1, 2, 0, 3, 0], [0, 0, 0, 4, 5]])))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "qJAicy1zW1QT"
      },
      "source": [
        "Look-ahead mask to mask the future tokens in a sequence.\n",
        "We also mask out pad tokens.\n",
        "\n",
        "i.e. To predict the third word, only the first and second word will be used"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "HSVdD2zKWaXx"
      },
      "outputs": [],
      "source": [
        "def create_look_ahead_mask(x):\n",
        "  seq_len = tf.shape(x)[1]\n",
        "  look_ahead_mask = 1 - tf.linalg.band_part(tf.ones((seq_len, seq_len)), -1, 0)\n",
        "  padding_mask = create_padding_mask(x)\n",
        "  return tf.maximum(look_ahead_mask, padding_mask)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "colab_type": "code",
        "id": "xhwz9xzxWcod",
        "outputId": "cdf37e6d-13fa-4fbb-d07c-a46be7a31bc8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[[[[0. 1. 1. 1. 1.]\n",
            "   [0. 0. 1. 1. 1.]\n",
            "   [0. 0. 1. 1. 1.]\n",
            "   [0. 0. 1. 0. 1.]\n",
            "   [0. 0. 1. 0. 0.]]]], shape=(1, 1, 5, 5), dtype=float32)\n"
          ]
        }
      ],
      "source": [
        "print(create_look_ahead_mask(tf.constant([[1, 2, 0, 4, 5]])))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "TpR7kz4jFkPJ"
      },
      "source": [
        "### Positional encoding\n",
        "\n",
        "Since this model doesn't contain any recurrence or convolution, positional encoding is added to give the model some information about the relative position of the words in the sentence. \n",
        "\n",
        "The positional encoding vector is added to the embedding vector. Embeddings represent a token in a d-dimensional space where tokens with similar meaning will be closer to each other. But the embeddings do not encode the relative position of words in a sentence. So after adding the positional encoding, words will be closer to each other based on the *similarity of their meaning and their position in the sentence*, in the d-dimensional space.\n",
        "\n",
        "See the notebook on [positional encoding](https://github.com/tensorflow/examples/blob/master/community/en/position_encoding.ipynb) to learn more about it. The formula for calculating the positional encoding is as follows:\n",
        "\n",
        "$$\\Large{PE_{(pos, 2i)} = sin(pos / 10000^{2i / d_{model}})} $$\n",
        "$$\\Large{PE_{(pos, 2i+1)} = cos(pos / 10000^{2i / d_{model}})} $$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "-9Oibz2es-qW"
      },
      "outputs": [],
      "source": [
        "class PositionalEncoding(tf.keras.layers.Layer):\n",
        "\n",
        "  def __init__(self, position, d_model):\n",
        "    super(PositionalEncoding, self).__init__()\n",
        "    self.pos_encoding = self.positional_encoding(position, d_model)\n",
        "\n",
        "  def get_angles(self, position, i, d_model):\n",
        "    angles = 1 / tf.pow(10000, (2 * (i // 2)) / tf.cast(d_model, tf.float32))\n",
        "    return position * angles\n",
        "\n",
        "  def positional_encoding(self, position, d_model):\n",
        "    angle_rads = self.get_angles(\n",
        "        position=tf.range(position, dtype=tf.float32)[:, tf.newaxis],\n",
        "        i=tf.range(d_model, dtype=tf.float32)[tf.newaxis, :],\n",
        "        d_model=d_model)\n",
        "    # apply sin to even index in the array\n",
        "    sines = tf.math.sin(angle_rads[:, 0::2])\n",
        "    # apply cos to odd index in the array\n",
        "    cosines = tf.math.cos(angle_rads[:, 1::2])\n",
        "\n",
        "    pos_encoding = tf.concat([sines, cosines], axis=-1)\n",
        "    pos_encoding = pos_encoding[tf.newaxis, ...]\n",
        "    return tf.cast(pos_encoding, tf.float32)\n",
        "\n",
        "  def call(self, inputs):\n",
        "    return inputs + self.pos_encoding[:, :tf.shape(inputs)[1], :]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "colab_type": "code",
        "id": "UC_fQehi3_Yh",
        "outputId": "6e7256b9-e08e-4545-b9b4-806894219aa3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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ubXWHwJFortZRSEXriNt3N1sDSzSVjNlafN4Sj99vz0Wwt83W+qLYRMlBR/HM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\npZQaQP4XrNHoBZRdWLQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "\u003cFigure size 432x288 with 2 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sample_pos_encoding = PositionalEncoding(50, 512)\n",
        "\n",
        "plt.pcolormesh(sample_pos_encoding.pos_encoding.numpy()[0], cmap='RdBu')\n",
        "plt.xlabel('Depth')\n",
        "plt.xlim((0, 512))\n",
        "plt.ylabel('Position')\n",
        "plt.colorbar()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "HVazCemoW2Ye"
      },
      "source": [
        "### Encoder Layer\n",
        "\n",
        "Each encoder layer consists of sublayers:\n",
        "\n",
        "1. Multi-head attention (with padding mask) \n",
        "2. 2 dense layers followed by dropout\n",
        "\n",
        "Each of these sublayers has a residual connection around it followed by a layer normalization. Residual connections help in avoiding the vanishing gradient problem in deep networks.\n",
        "\n",
        "The output of each sublayer is `LayerNorm(x + Sublayer(x))`. The normalization is done on the `d_model` (last) axis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "5guJOLJmfcuX"
      },
      "outputs": [],
      "source": [
        "def encoder_layer(units, d_model, num_heads, dropout, name=\"encoder_layer\"):\n",
        "  inputs = tf.keras.Input(shape=(None, d_model), name=\"inputs\")\n",
        "  padding_mask = tf.keras.Input(shape=(1, 1, None), name=\"padding_mask\")\n",
        "\n",
        "  attention = MultiHeadAttention(\n",
        "      d_model, num_heads, name=\"attention\")({\n",
        "          'query': inputs,\n",
        "          'key': inputs,\n",
        "          'value': inputs,\n",
        "          'mask': padding_mask\n",
        "      })\n",
        "  attention = tf.keras.layers.Dropout(rate=dropout)(attention)\n",
        "  attention = tf.keras.layers.LayerNormalization(\n",
        "      epsilon=1e-6)(inputs + attention)\n",
        "\n",
        "  outputs = tf.keras.layers.Dense(units=units, activation='relu')(attention)\n",
        "  outputs = tf.keras.layers.Dense(units=d_model)(outputs)\n",
        "  outputs = tf.keras.layers.Dropout(rate=dropout)(outputs)\n",
        "  outputs = tf.keras.layers.LayerNormalization(\n",
        "      epsilon=1e-6)(attention + outputs)\n",
        "\n",
        "  return tf.keras.Model(\n",
        "      inputs=[inputs, padding_mask], outputs=outputs, name=name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1106
        },
        "colab_type": "code",
        "id": "K16BIGSKfkve",
        "outputId": "9374deac-bb8a-458d-8a63-3cc07ef5c026"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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O97GVJ+bm5pMmTYqPj6d/ugEALUC90HGVLtcLuhybMp49eyYQCJghMF1cXOSb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\nVxkbG2/evHnLli1aDgn0Cr7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ip7nYuqL9xxqDvkxmXDe6yHX6+YEpjS4uLv3791+wYMHW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\nRQIAAAAA+qCoqMjOzk61fcViMZoA+ipVmgDOnDk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yy/jx4/l8vra29qpVq/Lz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6tGk7O9iKenZ3Z29rJl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\na1LMwYFuu3Tpkr+/f1x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ECXw+X/KIsjIrKiqiKIrP53fQJigoaNiwYfv37792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\nxa/fjzKjf//v8w87AADp98/74CkAAABQIjY2NpqamtJ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            "text/plain": [
              "\u003cIPython.core.display.Image object\u003e"
            ]
          },
          "execution_count": 20,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sample_encoder_layer = encoder_layer(\n",
        "    units=512,\n",
        "    d_model=128,\n",
        "    num_heads=4,\n",
        "    dropout=0.3,\n",
        "    name=\"sample_encoder_layer\")\n",
        "\n",
        "tf.keras.utils.plot_model(\n",
        "    sample_encoder_layer, to_file='encoder_layer.png', show_shapes=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "9r8lWGClfi_1"
      },
      "source": [
        "### Encoder\n",
        "\n",
        "The Encoder consists of:\n",
        "1.   Input Embedding\n",
        "2.   Positional Encoding\n",
        "3.   `num_layers` encoder layers\n",
        "\n",
        "The input is put through an embedding which is summed with the positional encoding. The output of this summation is the input to the encoder layers. The output of the encoder is the input to the decoder."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "LRfugon5Wy-Y"
      },
      "outputs": [],
      "source": [
        "def encoder(vocab_size,\n",
        "            num_layers,\n",
        "            units,\n",
        "            d_model,\n",
        "            num_heads,\n",
        "            dropout,\n",
        "            name=\"encoder\"):\n",
        "  inputs = tf.keras.Input(shape=(None,), name=\"inputs\")\n",
        "  padding_mask = tf.keras.Input(shape=(1, 1, None), name=\"padding_mask\")\n",
        "\n",
        "  embeddings = tf.keras.layers.Embedding(vocab_size, d_model)(inputs)\n",
        "  embeddings *= tf.math.sqrt(tf.cast(d_model, tf.float32))\n",
        "  embeddings = PositionalEncoding(vocab_size, d_model)(embeddings)\n",
        "\n",
        "  outputs = tf.keras.layers.Dropout(rate=dropout)(embeddings)\n",
        "\n",
        "  for i in range(num_layers):\n",
        "    outputs = encoder_layer(\n",
        "        units=units,\n",
        "        d_model=d_model,\n",
        "        num_heads=num_heads,\n",
        "        dropout=dropout,\n",
        "        name=\"encoder_layer_{}\".format(i),\n",
        "    )([outputs, padding_mask])\n",
        "\n",
        "  return tf.keras.Model(\n",
        "      inputs=[inputs, padding_mask], outputs=outputs, name=name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 755
        },
        "colab_type": "code",
        "id": "bNxCnjrvglnx",
        "outputId": "75eb3959-2f5f-45f8-f077-7dbfbbad8101"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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hxowZsLOzw7p162Bubt7q62Js/WmuyMhIuLi4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\nRU5ErkJggg==\n",
            "text/plain": [
              "\u003cIPython.core.display.Image object\u003e"
            ]
          },
          "execution_count": 22,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sample_encoder = encoder(\n",
        "    vocab_size=8192,\n",
        "    num_layers=2,\n",
        "    units=512,\n",
        "    d_model=128,\n",
        "    num_heads=4,\n",
        "    dropout=0.3,\n",
        "    name=\"sample_encoder\")\n",
        "\n",
        "tf.keras.utils.plot_model(\n",
        "   sample_encoder, to_file='encoder.png', show_shapes=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "af66azvgW9P-"
      },
      "source": [
        "### Decoder Layer\n",
        "\n",
        "Each decoder layer consists of sublayers:\n",
        "\n",
        "1.   Masked multi-head attention (with look ahead mask and padding mask)\n",
        "2.   Multi-head attention (with padding mask). `value` and `key` receive the *encoder output* as inputs. `query` receives the *output from the masked multi-head attention sublayer.*\n",
        "3.   2 dense layers followed by dropout\n",
        "\n",
        "Each of these sublayers has a residual connection around it followed by a layer normalization. The output of each sublayer is `LayerNorm(x + Sublayer(x))`. The normalization is done on the `d_model` (last) axis.\n",
        "\n",
        "As `query` receives the output from decoder's first attention block, and `key` receives the encoder output, the attention weights represent the importance given to the decoder's input based on the encoder's output. In other words, the decoder predicts the next word by looking at the encoder output and self-attending to its own output. See the demonstration above in the scaled dot product attention section."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "6mLvvNMWgDnf"
      },
      "outputs": [],
      "source": [
        "def decoder_layer(units, d_model, num_heads, dropout, name=\"decoder_layer\"):\n",
        "  inputs = tf.keras.Input(shape=(None, d_model), name=\"inputs\")\n",
        "  enc_outputs = tf.keras.Input(shape=(None, d_model), name=\"encoder_outputs\")\n",
        "  look_ahead_mask = tf.keras.Input(\n",
        "      shape=(1, None, None), name=\"look_ahead_mask\")\n",
        "  padding_mask = tf.keras.Input(shape=(1, 1, None), name='padding_mask')\n",
        "\n",
        "  attention1 = MultiHeadAttention(\n",
        "      d_model, num_heads, name=\"attention_1\")(inputs={\n",
        "          'query': inputs,\n",
        "          'key': inputs,\n",
        "          'value': inputs,\n",
        "          'mask': look_ahead_mask\n",
        "      })\n",
        "  attention1 = tf.keras.layers.LayerNormalization(\n",
        "      epsilon=1e-6)(attention1 + inputs)\n",
        "\n",
        "  attention2 = MultiHeadAttention(\n",
        "      d_model, num_heads, name=\"attention_2\")(inputs={\n",
        "          'query': attention1,\n",
        "          'key': enc_outputs,\n",
        "          'value': enc_outputs,\n",
        "          'mask': padding_mask\n",
        "      })\n",
        "  attention2 = tf.keras.layers.Dropout(rate=dropout)(attention2)\n",
        "  attention2 = tf.keras.layers.LayerNormalization(\n",
        "      epsilon=1e-6)(attention2 + attention1)\n",
        "\n",
        "  outputs = tf.keras.layers.Dense(units=units, activation='relu')(attention2)\n",
        "  outputs = tf.keras.layers.Dense(units=d_model)(outputs)\n",
        "  outputs = tf.keras.layers.Dropout(rate=dropout)(outputs)\n",
        "  outputs = tf.keras.layers.LayerNormalization(\n",
        "      epsilon=1e-6)(outputs + attention2)\n",
        "\n",
        "  return tf.keras.Model(\n",
        "      inputs=[inputs, enc_outputs, look_ahead_mask, padding_mask],\n",
        "      outputs=outputs,\n",
        "      name=name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1438
        },
        "colab_type": "code",
        "id": "8M1NrQ_NgEaM",
        "outputId": "c8390170-c774-4c9f-a015-174254cf99e4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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rZt28ZtrCP1rVmzhs/nHzt2rL6+Pi0tzcvL6/nnn9fz9P/6178SQi5fvlxX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9erWysrKhoWFeXl5iYqKJiQn/Zn/99dfw4cPHjx8voCvo0/79739Pnz5dSUnpzp07urq6TMcB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\n5cSJE3l5eb169SpxfFxcnCRJffr0qeiBEhI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S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\nOsEaCAAAAABQP6xcuXLo0KFdunSROwgAAKgTzKsFAAAAgHrg559/P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yZNUhfBNSUmJurr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            "text/plain": [
              "\u003cIPython.core.display.Image object\u003e"
            ]
          },
          "execution_count": 24,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sample_decoder_layer = decoder_layer(\n",
        "    units=512,\n",
        "    d_model=128,\n",
        "    num_heads=4,\n",
        "    dropout=0.3,\n",
        "    name=\"sample_decoder_layer\")\n",
        "\n",
        "tf.keras.utils.plot_model(\n",
        "    sample_decoder_layer, to_file='decoder_layer.png', show_shapes=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "NPSKnjS-gE_q"
      },
      "source": [
        "### Decoder\n",
        "\n",
        "The Decoder consists of:\n",
        "1.   Output Embedding\n",
        "2.   Positional Encoding\n",
        "3.   N decoder layers\n",
        "\n",
        "The target is put through an embedding which is summed with the positional encoding. The output of this summation is the input to the decoder layers. The output of the decoder is the input to the final linear layer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "dYRx7YzCW4bu"
      },
      "outputs": [],
      "source": [
        "def decoder(vocab_size,\n",
        "            num_layers,\n",
        "            units,\n",
        "            d_model,\n",
        "            num_heads,\n",
        "            dropout,\n",
        "            name='decoder'):\n",
        "  inputs = tf.keras.Input(shape=(None,), name='inputs')\n",
        "  enc_outputs = tf.keras.Input(shape=(None, d_model), name='encoder_outputs')\n",
        "  look_ahead_mask = tf.keras.Input(\n",
        "      shape=(1, None, None), name='look_ahead_mask')\n",
        "  padding_mask = tf.keras.Input(shape=(1, 1, None), name='padding_mask')\n",
        "  \n",
        "  embeddings = tf.keras.layers.Embedding(vocab_size, d_model)(inputs)\n",
        "  embeddings *= tf.math.sqrt(tf.cast(d_model, tf.float32))\n",
        "  embeddings = PositionalEncoding(vocab_size, d_model)(embeddings)\n",
        "\n",
        "  outputs = tf.keras.layers.Dropout(rate=dropout)(embeddings)\n",
        "\n",
        "  for i in range(num_layers):\n",
        "    outputs = decoder_layer(\n",
        "        units=units,\n",
        "        d_model=d_model,\n",
        "        num_heads=num_heads,\n",
        "        dropout=dropout,\n",
        "        name='decoder_layer_{}'.format(i),\n",
        "    )(inputs=[outputs, enc_outputs, look_ahead_mask, padding_mask])\n",
        "\n",
        "  return tf.keras.Model(\n",
        "      inputs=[inputs, enc_outputs, look_ahead_mask, padding_mask],\n",
        "      outputs=outputs,\n",
        "      name=name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 775
        },
        "colab_type": "code",
        "id": "tUdK8jb9hlTZ",
        "outputId": "e98b31bb-31ed-41e3-85ac-29762a62b367"
      },
      "outputs": [
        {
          "data": {
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Rx8dXV1YsqUKWL79u0damd71/zG/VlJSYkoKioSd911l3B3dxeV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\nR4/i2LFjyMjIgIODg+I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            "text/plain": [
              "\u003cIPython.core.display.Image object\u003e"
            ]
          },
          "execution_count": 26,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sample_decoder = decoder(\n",
        "    vocab_size=8192,\n",
        "    num_layers=2,\n",
        "    units=512,\n",
        "    d_model=128,\n",
        "    num_heads=4,\n",
        "    dropout=0.3,\n",
        "    name=\"sample_decoder\")\n",
        "\n",
        "tf.keras.utils.plot_model(\n",
        "    sample_decoder, to_file='decoder.png', show_shapes=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "yl0o97RJXAqw"
      },
      "source": [
        "### Transformer\n",
        "\n",
        "Transformer consists of the encoder, decoder and a final linear layer. The output of the decoder is the input to the linear layer and its output is returned."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "TW-v7Fz6XAfC"
      },
      "outputs": [],
      "source": [
        "def transformer(vocab_size,\n",
        "                num_layers,\n",
        "                units,\n",
        "                d_model,\n",
        "                num_heads,\n",
        "                dropout,\n",
        "                name=\"transformer\"):\n",
        "  inputs = tf.keras.Input(shape=(None,), name=\"inputs\")\n",
        "  dec_inputs = tf.keras.Input(shape=(None,), name=\"dec_inputs\")\n",
        "\n",
        "  enc_padding_mask = tf.keras.layers.Lambda(\n",
        "      create_padding_mask, output_shape=(1, 1, None),\n",
        "      name='enc_padding_mask')(inputs)\n",
        "  # mask the future tokens for decoder inputs at the 1st attention block\n",
        "  look_ahead_mask = tf.keras.layers.Lambda(\n",
        "      create_look_ahead_mask,\n",
        "      output_shape=(1, None, None),\n",
        "      name='look_ahead_mask')(dec_inputs)\n",
        "  # mask the encoder outputs for the 2nd attention block\n",
        "  dec_padding_mask = tf.keras.layers.Lambda(\n",
        "      create_padding_mask, output_shape=(1, 1, None),\n",
        "      name='dec_padding_mask')(inputs)\n",
        "\n",
        "  enc_outputs = encoder(\n",
        "      vocab_size=vocab_size,\n",
        "      num_layers=num_layers,\n",
        "      units=units,\n",
        "      d_model=d_model,\n",
        "      num_heads=num_heads,\n",
        "      dropout=dropout,\n",
        "  )(inputs=[inputs, enc_padding_mask])\n",
        "\n",
        "  dec_outputs = decoder(\n",
        "      vocab_size=vocab_size,\n",
        "      num_layers=num_layers,\n",
        "      units=units,\n",
        "      d_model=d_model,\n",
        "      num_heads=num_heads,\n",
        "      dropout=dropout,\n",
        "  )(inputs=[dec_inputs, enc_outputs, look_ahead_mask, dec_padding_mask])\n",
        "\n",
        "  outputs = tf.keras.layers.Dense(units=vocab_size, name=\"outputs\")(dec_outputs)\n",
        "\n",
        "  return tf.keras.Model(inputs=[inputs, dec_inputs], outputs=outputs, name=name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "colab_type": "code",
        "id": "aihJLVq_iJ_T",
        "outputId": "de621fd6-c2ce-4c0e-8cf9-57f35a33e5eb"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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u7qNHj6i5wod33bp1qqqqx44da21tjYmJUVBQuHr1qiiBac+2YcMG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\nAKCZUKlUcXFxo0aNqv4uO3bsG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            "text/plain": [
              "\u003cIPython.core.display.Image object\u003e"
            ]
          },
          "execution_count": 28,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sample_transformer = transformer(\n",
        "    vocab_size=8192,\n",
        "    num_layers=4,\n",
        "    units=512,\n",
        "    d_model=128,\n",
        "    num_heads=4,\n",
        "    dropout=0.3,\n",
        "    name=\"sample_transformer\")\n",
        "\n",
        "tf.keras.utils.plot_model(\n",
        "    sample_transformer, to_file='transformer.png', show_shapes=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "9HD7GK-nh_KT"
      },
      "source": [
        "## Train model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "PDDxNpA-5Q5t"
      },
      "source": [
        "### Initialize model\n",
        "\n",
        "To keep this example small and relatively fast, the values for *num_layers, d_model, and units* have been reduced. See the [paper](https://arxiv.org/abs/1706.03762) for all the other versions of the transformer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "xE3unrOT5M5z"
      },
      "outputs": [],
      "source": [
        "tf.keras.backend.clear_session()\n",
        "\n",
        "# Hyper-parameters\n",
        "NUM_LAYERS = 2\n",
        "D_MODEL = 256\n",
        "NUM_HEADS = 8\n",
        "UNITS = 512\n",
        "DROPOUT = 0.1\n",
        "\n",
        "model = transformer(\n",
        "    vocab_size=VOCAB_SIZE,\n",
        "    num_layers=NUM_LAYERS,\n",
        "    units=UNITS,\n",
        "    d_model=D_MODEL,\n",
        "    num_heads=NUM_HEADS,\n",
        "    dropout=DROPOUT)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "0_GCb0LaV1tI"
      },
      "source": [
        "### Loss function\n",
        "\n",
        "Since the target sequences are padded, it is important to apply a padding mask when calculating the loss."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "UInVM9iGAMv1"
      },
      "outputs": [],
      "source": [
        "def loss_function(y_true, y_pred):\n",
        "  y_true = tf.reshape(y_true, shape=(-1, MAX_LENGTH - 1))\n",
        "  \n",
        "  loss = tf.keras.losses.SparseCategoricalCrossentropy(\n",
        "      from_logits=True, reduction='none')(y_true, y_pred)\n",
        "\n",
        "  mask = tf.cast(tf.not_equal(y_true, 0), tf.float32)\n",
        "  loss = tf.multiply(loss, mask)\n",
        "\n",
        "  return tf.reduce_mean(loss)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "XvFM9ajSVybP"
      },
      "source": [
        "### Custom learning rate\n",
        "\n",
        "Use the Adam optimizer with a custom learning rate scheduler according to the formula in the [paper](https://arxiv.org/abs/1706.03762).\n",
        "\n",
        "$$\\Large{lrate = d_{model}^{-0.5} * min(step{\\_}num^{-0.5}, step{\\_}num * warmup{\\_}steps^{-1.5})}$$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "WW3SeLDhAMJd"
      },
      "outputs": [],
      "source": [
        "class CustomSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):\n",
        "\n",
        "  def __init__(self, d_model, warmup_steps=4000):\n",
        "    super(CustomSchedule, self).__init__()\n",
        "\n",
        "    self.d_model = d_model\n",
        "    self.d_model = tf.cast(self.d_model, tf.float32)\n",
        "\n",
        "    self.warmup_steps = warmup_steps\n",
        "\n",
        "  def __call__(self, step):\n",
        "    arg1 = tf.math.rsqrt(step)\n",
        "    arg2 = step * (self.warmup_steps**-1.5)\n",
        "\n",
        "    return tf.math.rsqrt(self.d_model) * tf.math.minimum(arg1, arg2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "colab_type": "code",
        "id": "67BoG_UeaHHw",
        "outputId": "aa94e9c8-eadd-42f2-fa26-191a2c3c0fcc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Train Step')"
            ]
          },
          "execution_count": 32,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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hjTfcz4e+v4x/f3wjhzr1pAeRYjEmrmcxsynApcDbBsrr7jcDNwO0t7cPqQ+n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\nCjAiIhILBRgREYmFAoyIiMTi/wMZIzDctONGFgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cFigure size 432x288 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sample_learning_rate = CustomSchedule(d_model=128)\n",
        "\n",
        "plt.plot(sample_learning_rate(tf.range(200000, dtype=tf.float32)))\n",
        "plt.ylabel(\"Learning Rate\")\n",
        "plt.xlabel(\"Train Step\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "cCqve3kwWCxd"
      },
      "source": [
        "### Compile Model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "1QqojIa5WEQq"
      },
      "outputs": [],
      "source": [
        "learning_rate = CustomSchedule(D_MODEL)\n",
        "\n",
        "optimizer = tf.keras.optimizers.Adam(\n",
        "    learning_rate, beta_1=0.9, beta_2=0.98, epsilon=1e-9)\n",
        "\n",
        "def accuracy(y_true, y_pred):\n",
        "  # ensure labels have shape (batch_size, MAX_LENGTH - 1)\n",
        "  y_true = tf.reshape(y_true, shape=(-1, MAX_LENGTH - 1))\n",
        "  accuracy = tf.metrics.SparseCategoricalAccuracy()(y_true, y_pred)\n",
        "  return accuracy\n",
        "\n",
        "model.compile(optimizer=optimizer, loss=loss_function, metrics=[accuracy])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "vDMd69urLNuc"
      },
      "source": [
        "### Fit model\n",
        "\n",
        "Train our transformer by simply calling `model.fit()`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "colab_type": "code",
        "id": "d7iahRzlLNG2",
        "outputId": "12001b91-4453-48e1-97db-b4ac0d708364"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "689/689 [==============================] - 97s 141ms/step - loss: 2.1146 - accuracy: 0.0249\n",
            "Epoch 2/20\n",
            "689/689 [==============================] - 81s 118ms/step - loss: 1.5008 - accuracy: 0.0530\n",
            "Epoch 3/20\n",
            "689/689 [==============================] - 82s 119ms/step - loss: 1.3940 - accuracy: 0.0653\n",
            "Epoch 4/20\n",
            "689/689 [==============================] - 82s 118ms/step - loss: 1.3313 - accuracy: 0.0719\n",
            "Epoch 5/20\n",
            "689/689 [==============================] - 82s 119ms/step - loss: 1.2744 - accuracy: 0.0765\n",
            "Epoch 6/20\n",
            "689/689 [==============================] - 82s 119ms/step - loss: 1.2223 - accuracy: 0.0801\n",
            "Epoch 7/20\n",
            "689/689 [==============================] - 82s 118ms/step - loss: 1.1670 - accuracy: 0.0832\n",
            "Epoch 8/20\n",
            "689/689 [==============================] - 82s 119ms/step - loss: 1.1050 - accuracy: 0.0861\n",
            "Epoch 9/20\n",
            "689/689 [==============================] - 82s 119ms/step - loss: 1.0503 - accuracy: 0.0889\n",
            "Epoch 10/20\n",
            "689/689 [==============================] - 82s 120ms/step - loss: 1.0002 - accuracy: 0.0917\n",
            "Epoch 11/20\n",
            "689/689 [==============================] - 82s 118ms/step - loss: 0.9540 - accuracy: 0.0945\n",
            "Epoch 12/20\n",
            "689/689 [==============================] - 82s 119ms/step - loss: 0.9122 - accuracy: 0.0973\n",
            "Epoch 13/20\n",
            "689/689 [==============================] - 82s 118ms/step - loss: 0.8744 - accuracy: 0.1001\n",
            "Epoch 14/20\n",
            "689/689 [==============================] - 82s 119ms/step - loss: 0.8396 - accuracy: 0.1029\n",
            "Epoch 15/20\n",
            "689/689 [==============================] - 82s 119ms/step - loss: 0.8082 - accuracy: 0.1056\n",
            "Epoch 16/20\n",
            "689/689 [==============================] - 82s 119ms/step - loss: 0.7799 - accuracy: 0.1082\n",
            "Epoch 17/20\n",
            "689/689 [==============================] - 82s 118ms/step - loss: 0.7540 - accuracy: 0.1108\n",
            "Epoch 18/20\n",
            "689/689 [==============================] - 82s 119ms/step - loss: 0.7300 - accuracy: 0.1134\n",
            "Epoch 19/20\n",
            "689/689 [==============================] - 82s 119ms/step - loss: 0.7076 - accuracy: 0.1158\n",
            "Epoch 20/20\n",
            "689/689 [==============================] - 82s 118ms/step - loss: 0.6881 - accuracy: 0.1183\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "\u003ctensorflow.python.keras.callbacks.History at 0x7f6a7a4b8198\u003e"
            ]
          },
          "execution_count": 34,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "EPOCHS = 20\n",
        "\n",
        "model.fit(dataset, epochs=EPOCHS)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "p1DUXog6WqV-"
      },
      "source": [
        "## Evaluate and predict\n",
        "\n",
        "The following steps are used for evaluation:\n",
        "\n",
        "* Apply the same preprocessing method we used to create our dataset for the input sentence.\n",
        "* Tokenize the input sentence and add `START_TOKEN` and `END_TOKEN`. \n",
        "* Calculate the padding masks and the look ahead masks.\n",
        "* The decoder then outputs the predictions by looking at the encoder output and its own output.\n",
        "* Select the last word and calculate the argmax of that.\n",
        "* Concatentate the predicted word to the decoder input as pass it to the decoder.\n",
        "* In this approach, the decoder predicts the next word based on the previous words it predicted.\n",
        "\n",
        "Note: The model used here has less capacity and trained on a subset of the full dataset, hence its performance can be further improved."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "_NjsS3zuAbRn"
      },
      "outputs": [],
      "source": [
        "def evaluate(sentence):\n",
        "  sentence = preprocess_sentence(sentence)\n",
        "\n",
        "  sentence = tf.expand_dims(\n",
        "      START_TOKEN + tokenizer.encode(sentence) + END_TOKEN, axis=0)\n",
        "\n",
        "  output = tf.expand_dims(START_TOKEN, 0)\n",
        "\n",
        "  for i in range(MAX_LENGTH):\n",
        "    predictions = model(inputs=[sentence, output], training=False)\n",
        "\n",
        "    # select the last word from the seq_len dimension\n",
        "    predictions = predictions[:, -1:, :]\n",
        "    predicted_id = tf.cast(tf.argmax(predictions, axis=-1), tf.int32)\n",
        "\n",
        "    # return the result if the predicted_id is equal to the end token\n",
        "    if tf.equal(predicted_id, END_TOKEN[0]):\n",
        "      break\n",
        "\n",
        "    # concatenated the predicted_id to the output which is given to the decoder\n",
        "    # as its input.\n",
        "    output = tf.concat([output, predicted_id], axis=-1)\n",
        "\n",
        "  return tf.squeeze(output, axis=0)\n",
        "\n",
        "\n",
        "def predict(sentence):\n",
        "  prediction = evaluate(sentence)\n",
        "\n",
        "  predicted_sentence = tokenizer.decode(\n",
        "      [i for i in prediction if i \u003c tokenizer.vocab_size])\n",
        "\n",
        "  print('Input: {}'.format(sentence))\n",
        "  print('Output: {}'.format(predicted_sentence))\n",
        "\n",
        "  return predicted_sentence"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "9J3Jdtk2P-RT"
      },
      "source": [
        "Let's test our model!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "colab_type": "code",
        "id": "6IeMSGEgRTvC",
        "outputId": "72d09666-08c0-42c5-9060-f9c6d4c6c54c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Input: Where have you been?\n",
            "Output: i m not gonna bring you here .\n"
          ]
        }
      ],
      "source": [
        "output = predict('Where have you been?')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "colab_type": "code",
        "id": "ivVgU6ydRV8R",
        "outputId": "b659089d-0fde-428b-c808-f47600d08438"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Input: It's a trap\n",
            "Output: no , it s not .\n"
          ]
        }
      ],
      "source": [
        "output = predict(\"It's a trap\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "colab_type": "code",
        "id": "s5zG7i8KAtRU",
        "outputId": "2b2bcd61-8662-4019-fe1a-5ea2d3323b7c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Input: I am not crazy, my mother had me tested.\n",
            "Output: you re a good man , roy . that s a good man , roy , you re a little girl , that s a good man . you re a little girl .\n",
            "\n",
            "Input: you re a good man , roy . that s a good man , roy , you re a little girl , that s a good man . you re a little girl .\n",
            "Output: i m glad you re not a drug addict .\n",
            "\n",
            "Input: i m glad you re not a drug addict .\n",
            "Output: the inheritance .\n",
            "\n",
            "Input: the inheritance .\n",
            "Output: no , i don t know what to do . i just like to tell you .\n",
            "\n",
            "Input: no , i don t know what to do . i just like to tell you .\n",
            "Output: i m not sure . i m not gonna mention my name .\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# feed the model with its previous output\n",
        "sentence = 'I am not crazy, my mother had me tested.'\n",
        "for _ in range(5):\n",
        "  sentence = predict(sentence)\n",
        "  print('')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "5-dGPc14QnAP"
      },
      "source": [
        "## Summary\n",
        "\n",
        "Here we are, we have implemented a Transformer in TensorFlow 2.0 in around 500 lines of code.\n",
        "\n",
        "In this tutorial, we focus on the two different approaches to implement complex models with Functional API and Model subclassing, and how to incorporate them.\n",
        "\n",
        "Try using a different dataset or hyper-parameters to train the Transformer! Thanks for reading.\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "transformer_chatbot.ipynb",
      "provenance": [],
      "toc_visible": true,
      "version": "0.3.2"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
